diff --git a/DESCRIPTION b/DESCRIPTION
index 366e1d3..fde5c6b 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,5 +1,5 @@
 Package: Ibex
-Title: Methods for BCR single-cell embedding
+Title: Methods for BCR single-cell embedding 
 Version: 1.1.1
 Authors@R: c(
     person(given = "Nick", family = "Borcherding", role = c("aut", "cre", "cph"), email = "ncborch@gmail.com"),
diff --git a/NEWS.md b/NEWS.md
index 3cfe81a..4e2117c 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -7,6 +7,20 @@
 * Updated CI workflows to target `devel` branch
 * Converted NEWS to NEWS.md format
 * Added automated GitHub Release workflow via tags
+
+## v1.0.0
+* Integration of Ibex with immApex
+* Updated Seurat object to v5
+* Updated support for SCE format for `runIbex()`
+* Update `CoNGAfy()` to function with all versions of Seurat
+* Updated `quietBCRgenes()` to use VariableFeatures() call for SeuratV5 and backward compatibility
+* Added `getHumanIgPseudoGenes()` to return a list of human Immunoglobulin Pseudo genes
+* Added new light and heavy chain models with encoding methods: OHE, atchleyFactors, crucianiProperties, kideraFactors, MSWHIM, tScales, zScales
+* Trained convolutional and variational autoencoders (architecture: 512-256-128-256-512)
+* Implementing GitHub action workflows
+* Adding testthat framework
+* Deprecated clonalCommunity
+* Added geometric encoding using the BLOSUM62 matrix
 * `Ibex_matrix()` now accepts character vectors of amino acid sequences directly
 * Removed rlang from Imports, added lifecycle
 * As per `basilisk` documentation:
diff --git a/README.md b/README.md
index 841ec16..0ed7ded 100644
--- a/README.md
+++ b/README.md
@@ -11,19 +11,16 @@ Using BCR sequences for graph embedding
 [![Bioc Downloads](http://www.bioconductor.org/shields/downloads/release/Ibex.svg)](http://bioconductor.org/packages/stats/bioc/Ibex/)
 <!-- badges: end -->
 
-<img align="right" src="https://github.com/BorchLab/Ibex/blob/main/www/ibex_hex.png" width="305" height="352">
-
-## Introduction
+<img align="right" src="https://github.com/BorchLab/Ibex/blob/devel/www/ibex_hex.png" width="305" height="352">
 
 Single-cell sequencing is an integral tool in immunology and oncology, enabling researchers to measure gene expression and immune cell receptor profiling at the level of individual cells. We developed the [scRepertoire](https://github.com/BorchLab/scRepertoire) R package to facilitate the integration of immune receptor and gene expression data. However, leveraging clonal indices for more complex analyses—such as using clonality in cell embedding—remains challenging.
 
 **Ibex** addresses this need by using deep learning to vectorize BCR sequences based on amino acid properties or their underlying order. Ibex is the sister package to [Trex](https://github.com/BorchLab/Trex), which focuses on TCR sequence data.
 
-## System Requirements
-
-Ibex has been tested on R versions >= 4.0. For details on required R packages, refer to the package's DESCRIPTION file. It is designed to work with single-cell objects containing BCR data generated using [scRepertoire](https://github.com/BorchLab/scRepertoire). Ibex has been tested on macOS and Linux.
+### System Requirements 
+Ibex has been tested on R versions >= 4.0. For details on required R packages, refer to the package’s DESCRIPTION file. It is designed to work with single-cell objects containing BCR data generated using [scRepertoire](https://github.com/BorchLab/scRepertoire). Ibex has been tested on macOS and Linux.
 
-## Installation
+### Installation
 
 Ibex relies on the [immApex](https://github.com/BorchLab/immApex) API can be installed directly from GitHub:
 
@@ -54,7 +51,7 @@ BiocManager::install("Ibex")
 
 The main version of Ibex is submitted to Bioconductor (installation instructions will be updated after review). By default, Ibex will automatically pull deep learning models from a [Zenodo repository](https://zenodo.org/records/14919286) and cache them locally.
 
-## Usage/Demos
+### Usage/Demos
 
 Ibex integrates smoothly into most popular R-based single-cell workflows, including **Seurat** and **Bioconductor/SingleCellExperiment.**
 
@@ -64,7 +61,7 @@ See the [vignette](https://www.borch.dev/uploads/screpertoire/articles/ibex) for
 
 <img align="center" src="https://github.com/BorchLab/Ibex/blob/main/www/graphicalAbstract.png">
 
-### Autoencoded Matrix
+#### Autoencoded Matrix
 
 The Ibex algorithm allows users to select BCR-based metrics to return autoencoded values to be used in dimensional reduction. If single-cell objects are not filtered for B cells with BCR,  `Ibex_matrix()` will still return values, however IBEX_1 will be based on the disparity of BCR-containing and BCR-non-containing cells based on the Ibex algorithm.
 
@@ -73,7 +70,7 @@ library(Ibex)
 my_ibex <- Ibex_matrix(singleObject)
 ```
 
-### Seurat or Single-Cell Experiment
+#### Seurat or Single-Cell Experiment
 
 You can run Ibex within your Seurat or Single-Cell Experiemt workflow. **Importantly** `runIbex()` will automatically filter single-cells that do not contain BCR information in the meta data of the single-cell object.
 
@@ -90,7 +87,7 @@ seuratObj_Bonly <- runIbex(seuratObj, #The single cell object
 seuratObj_Bonly <- runIbex(seuratObj, reduction.name = "Ibex")
 ```
 
-### After Running Ibex
+#### After Running Ibex
 
 Once the Ibex embeddings are part of your Seurat object, you can use these embeddings to generate a t-SNE or UMAP:
 
@@ -109,7 +106,6 @@ seuratObj <- RunPCA(seuratObj)
 ```
 
 #### Running WNN approach
-
 ```r
 seuratObj <- FindMultiModalNeighbors(seuratObj,
                                      reduction.list = list("pca", "Ibex"),